We use particle output and halo catalogs from the CrowCanyon N-body simulations to test the HZPT model on matter and halo statistics.
These simulations were run using FastPM [1]} with \(N_{p} = 6144^{3}\)  particles, a box size of \(L_{\rm {box}} = 3200 \ \rm {Mpc}/h\) , a boost factor \(B=2\)  (for a \(12288^{3}\)  PM force grid and Nyquist wavenumber \(k_{\rm {Nyq}} \approx 12~h/\mathrm {Mpc}\) ), using the Planck15 cosmological parameters [2]} (without neutrino effects).
CrowCanyon halos were identified using the nbodykit FoF halo finder [3]} with linking length \(b=0.2\) .
We computed simulation power spectra using nbodykit [4]} with a FFT mesh using \(N_{\rm {mesh}} = 2048\)  (\(k_{\rm {Nyq}} \approx 2 ~h/\rm {Mpc}\) ) using a correction for compensation [5]}, and a Triangular Shaped Cloud interpolation window.
Similarly, we computed correlation functions using FFTs on large scales with \(N_{\rm {mesh}} = 1024\) , matched at \(r=10 \ \rm {Mpc}/h\)  to the result of the corrfunc [6]} pair counting algorithm (as included in nbodykit) with 100 logarithmically-spaced bins and a maximum bin of \(10\ \rm {Mpc} /h\) .
Power spectra are sample variance cancelled using unitary amplitude (“paired-fixed”) power spectra [7]} at the same random seed as the N-body simulation initial conditions.
To compute the linear theory power spectrum we use CLASS [8]}.
To quickly compute the Zeldovich power spectrum we modify a version of the FFTLog-based code employed in [9]} and [10]}.
Fits are performed using the scipy implementation of the “Trust Region Reflective” optimization algorithm.
